We present a comprehensive workflow for processing a large-scale airborne Topo-Bathymetric LiDAR (TBL) dataset acquired over the 28 km Ardèche River Gorges in France during October 2021. To address limited depth penetration, low signal-to-noise ratio, and complex topography, we integrate onboard discrete returns, a full-waveform (FWF) re-analysis, and an orthorectified full-waveform synthesis (OrthoFWF). Depth penetration, evaluated by <em>D</em><sub>99</sub> (99<sup>th</sup> percentile of retrieved water depth, <em>D</em>), increases from 2.88 m (discrete) to 3.70 m (FWF) and 4.48 m (OrthoFWF). Area coverage increases from 70.1 % to 79.5 % to 85.6 % of the submerged area with bathymetric data available, while the length-based coverage (the percentage of river length composed of reaches with ≥95 % bathymetric coverage) improves from 31.8 % to 54.7 % to 86.9 %. A new unsupervised classification method, using a kernel-density–derived intensity threshold, was applied to 1.3 million points, enhancing the separation of bed returns from noise within the OrthoFWF domain and improving depth extraction. The workflow includes internal flight-line geometric correction, precision benchmarking against France’s national LiDAR HD dataset, bathymetric classification with a random-forest classifier, and a targeted supplementary sonar survey to constrain the deepest reaches. To produce the final Digital Elevation Model from incomplete coverage, we compare three interpolation approaches and demonstrate that Poisson surface reconstruction yields the most morphologically realistic surfaces, particularly when constrained by sonar-derived depths. This integrated workflow substantially improves the accuracy and completeness of river bathymetry, supporting high-fidelity hydrodynamic modeling and advancing TBL applications in fluvial geomorphology.